Mahout is a set of libraries for running machine learning processes,
such as recommendation, clustering and categorisation.

The libraries work against an abstract model that can be anything from a
file to a full Hadoop cluster. This means you can start playing around
with small data sets in files, a local database, a Hadoop cluster or a
custom data store.

After a bit of research, it turned out not to be too complex to call
via any JVM language. When you compile and install Mahout, the libraries
are installed into your local Maven cache. This makes it very easy to
include them into any JVM type project.

To help work through the various features, I’m reading the early access
edition of Mahout in Action. I am trying out the examples in
Clojure as I read through.

To get started, the following steps will setup a Clojure project to work
with Mahout: